Since their inception, many computer systems, particularly business systems, have been used primarily to capture, store and report on data associated with individual transactions of some type, such as health care claims, bank deposits, or purchase orders. These systems have been very successful in automating manual procedures, but have created a huge volume of stored data that is not being adequately utilized to make qualitative decisions (e.g. business decisions).
In recent years it has become widely recognized in many industries that a compelling need exists to better leverage the investments made in the vast accumulation of data. The trend in this regard is to drift from the prevailing microscopic examination of data, transaction by transaction, in favor of a higher level, retrospective evaluation of data.
This need has spawned a generation of so-called decision support and expert systems designed to assist the human decision making process. In some cases these systems use a rudimentary method of encoding human knowledge to process data and, in other cases, the informational value of data is increased through the use of various data navigation tools and techniques.
While these systems have achieved some level of success, there still remains large classes of problems that are yet unsolved. One such class of problems is the assessment of behavior in determining meaningful profiles of entities relative to others within the same general peer group. Attempts at conducting computerized behavior profiling have been made, particularly in the health care industry, with little success.
One major difficulty in programming behavior profiling stems from a limiting factor present in most modern digital computer systems, binary logic. Digital computers, and the programming languages used to program them, are based on a logic system that supports only two truth states, represented as 0 (false) and 1 (true). This constraint poses significant challenges in dealing with large combinatorial problems that are more properly represented with a multi- valued logic.
Searching health care claims to detect physician fraud illustrates the complexity of behavior profiling problems.
To start, there are hundreds of known behavior characteristics that indicate, but do not prove, physician fraud. Behavior that is characteristic of fraudulent activity varies by medical specialty and geography, and, changes frequently. In addition, the relative importance of different fraudulent behavior characteristics varies and it is very difficult, if not impossible, to quantify the differences.
Adding further to the dynamics of physician profiling, of any kind, are the affects of medical advancement, health care reform and numerous other social and economic factors. Technologies constrained by discrete comparisons and a two-valued logic system are inadequate for coping with the intractable nature of this kind of problem.
One approach to this problem has been to encode what is known about physician fraud in If-Then statements, or rules, that can be used to test the claims of a physician for the presence of fraud. Several practical problems exist with this approach once it is understood that there is no clear and manageable subset of things that physicians do when engaging in fraudulent activity.
There are a wide range of factors that need to be examined simultaneously, and the result must be a composite evaluation of the overall behavior of the physician. The number of If-Then rules needed to test discrete values, or ranges of values, taking dozens or hundreds of constantly changing factors into account is nearly impossible to create, and is surely impossible to maintain.
Mathematical or statistical modeling is an alternative, and popular, method of conducting behavior profiling analysis that has fewer drawbacks but still falls short of a practical solution.
In this case, statistical normality for a peer group is calculated for a small manageable number of behavior characteristics known about a subject, such as physician fraud. For each physician in the peer group, a rigorous mathematical calculation is used to measure the combined degree to which that physician deviates from normal behavior for all behavior characteristics. This approach involves calculating a summary of standard deviations that identify the statistical outliers within the peer group.
This approach benefits from the objectivity achieved through peer group analysis, as opposed to arbitrary threshold limits set by domain experts, but lacks the flexibility and extensibility required of a meaningful and practical solution.
A purely statistical approach to physician profiling has been shown to be computationally impractical when dealing with a large number of frequently changing behavior characteristics. Changing or adding behavior characteristics, and applying varying weights for behavior characteristics can only be achieved by applying rigid mathematical formulas requiring careful scrutiny by highly skilled mathematicians. In many computing environments, especially business computing environments, this kind of skill is rare, and in many cases non-existent, in sufficient quantity to cope with the dynamics of physician profiling.
Another drawback to this approach is the tendency of statistical modeling to lose access to the detailed information used to derive its conclusions. This is especially important in profiling physician fraud where the investigation and prosecution process necessarily requires very specific evidence of wrong- doing.
One other approach to physician profiling that is often discussed but has rarely, if ever, been tried is the use of neural network technology to `mine` claims databases to search for fraudulent physicians. In this case, a neural network would be provided a training set of known fraudulent profiles and it would be trained to recognize the characteristics of fraud by drawing relationships between the data elements in the training set. Once the training of the neural network is complete, it would then be used to scan the universe of physician data in search of physicians matching the neural networks `learned` understanding of fraud.
A major, perhaps fatal, problem exists with this approach for physician profiling and for most other non-trivial profiling applications as well. A training set of data simply does not exist that is nearly large enough, or stable enough, to support the training phase of a neural network. Again, the dynamics of physician profiling, as well as many other behavior profiling applications, render this approach unsuitable.
Complex behavior profiling requires a solution that is flexible, extensible, domain independent, and can be routinely implemented in varying types of computing environments with commonly available skills.